A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides

Angel Cruz-Roa; Fabio González; Joseph Galaro; Alexander R. Judkins; David Ellison; Jennifer Baccon; Anant Madabhushi; Eduardo Romero

doi:10.1007/978-3-642-33415-3_20

A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides

Angel Cruz-Roa, Fabio González, Joseph Galaro, Alexander R. Judkins, David Ellison, Jennifer Baccon, Anant Madabhushi, Eduardo Romero

Department of Pathology and Laboratory Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

23 Scopus citations

Abstract

A method for automatic analysis and interpretation of histopathology images is presented. The method uses a representation of the image data set based on bag of features histograms built from visual dictionary of Haar-based patches and a novel visual latent semantic strategy for characterizing the visual content of a set of images. One important contribution of the method is the provision of an interpretability layer, which is able to explain a particular classification by visually mapping the most important visual patterns associated with such classification. The method was evaluated on a challenging problem involving automated discrimination of medulloblastoma tumors based on image derived attributes from whole slide images as anaplastic or non-anaplastic. The data set comprised 10 labeled histopathological patient studies, 5 for anaplastic and 5 for non-anaplastic, where 750 square images cropped randomly from cancerous region from whole slide per study. The experimental results show that the new method is competitive in terms of classification accuracy achieving 0.87 in average.

Original language	English (US)
Title of host publication	Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings
Editors	Nicholas Ayache, Herve Delingette, Polina Golland, Kensaku Mori
Publisher	Springer Verlag
Pages	157-164
Number of pages	8
ISBN (Print)	9783642334146
DOIs	https://doi.org/10.1007/978-3-642-33415-3_20
State	Published - 2012
Event	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France Duration: Oct 1 2012 → Oct 5 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7510 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
Country/Territory	France
City	Nice
Period	10/1/12 → 10/5/12

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-642-33415-3_20

Cite this

Cruz-Roa, A., González, F., Galaro, J., Judkins, A. R., Ellison, D., Baccon, J., Madabhushi, A., & Romero, E. (2012). A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides. In N. Ayache, H. Delingette, P. Golland, & K. Mori (Eds.), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings (pp. 157-164). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7510 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-642-33415-3_20

Cruz-Roa, Angel ; González, Fabio ; Galaro, Joseph et al. / A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides. Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. editor / Nicholas Ayache ; Herve Delingette ; Polina Golland ; Kensaku Mori. Springer Verlag, 2012. pp. 157-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A method for automatic analysis and interpretation of histopathology images is presented. The method uses a representation of the image data set based on bag of features histograms built from visual dictionary of Haar-based patches and a novel visual latent semantic strategy for characterizing the visual content of a set of images. One important contribution of the method is the provision of an interpretability layer, which is able to explain a particular classification by visually mapping the most important visual patterns associated with such classification. The method was evaluated on a challenging problem involving automated discrimination of medulloblastoma tumors based on image derived attributes from whole slide images as anaplastic or non-anaplastic. The data set comprised 10 labeled histopathological patient studies, 5 for anaplastic and 5 for non-anaplastic, where 750 square images cropped randomly from cancerous region from whole slide per study. The experimental results show that the new method is competitive in terms of classification accuracy achieving 0.87 in average.",

author = "Angel Cruz-Roa and Fabio Gonz{\'a}lez and Joseph Galaro and Judkins, {Alexander R.} and David Ellison and Jennifer Baccon and Anant Madabhushi and Eduardo Romero",

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Cruz-Roa, A, González, F, Galaro, J, Judkins, AR, Ellison, D, Baccon, J, Madabhushi, A & Romero, E 2012, A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides. in N Ayache, H Delingette, P Golland & K Mori (eds), Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7510 LNCS, Springer Verlag, pp. 157-164, 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012, Nice, France, 10/1/12. https://doi.org/10.1007/978-3-642-33415-3_20

A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides. / Cruz-Roa, Angel; González, Fabio; Galaro, Joseph et al.
Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. ed. / Nicholas Ayache; Herve Delingette; Polina Golland; Kensaku Mori. Springer Verlag, 2012. p. 157-164 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7510 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Cruz-Roa, Angel

AU - González, Fabio

AU - Galaro, Joseph

AU - Judkins, Alexander R.

AU - Ellison, David

AU - Baccon, Jennifer

AU - Madabhushi, Anant

AU - Romero, Eduardo

N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2012.

PY - 2012

Y1 - 2012

N2 - A method for automatic analysis and interpretation of histopathology images is presented. The method uses a representation of the image data set based on bag of features histograms built from visual dictionary of Haar-based patches and a novel visual latent semantic strategy for characterizing the visual content of a set of images. One important contribution of the method is the provision of an interpretability layer, which is able to explain a particular classification by visually mapping the most important visual patterns associated with such classification. The method was evaluated on a challenging problem involving automated discrimination of medulloblastoma tumors based on image derived attributes from whole slide images as anaplastic or non-anaplastic. The data set comprised 10 labeled histopathological patient studies, 5 for anaplastic and 5 for non-anaplastic, where 750 square images cropped randomly from cancerous region from whole slide per study. The experimental results show that the new method is competitive in terms of classification accuracy achieving 0.87 in average.

AB - A method for automatic analysis and interpretation of histopathology images is presented. The method uses a representation of the image data set based on bag of features histograms built from visual dictionary of Haar-based patches and a novel visual latent semantic strategy for characterizing the visual content of a set of images. One important contribution of the method is the provision of an interpretability layer, which is able to explain a particular classification by visually mapping the most important visual patterns associated with such classification. The method was evaluated on a challenging problem involving automated discrimination of medulloblastoma tumors based on image derived attributes from whole slide images as anaplastic or non-anaplastic. The data set comprised 10 labeled histopathological patient studies, 5 for anaplastic and 5 for non-anaplastic, where 750 square images cropped randomly from cancerous region from whole slide per study. The experimental results show that the new method is competitive in terms of classification accuracy achieving 0.87 in average.

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Cruz-Roa A, González F, Galaro J, Judkins AR, Ellison D, Baccon J et al. A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides. In Ayache N, Delingette H, Golland P, Mori K, editors, Medical Image Computing and Computer-Assisted Intervention, MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 157-164. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-33415-3_20

A visual latent semantic approach for automatic analysis and interpretation of anaplastic medulloblastoma virtual slides

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